PVplr: R Package Implementation of Multiple Filters and Algorithms for Time-series Performance Loss Rate Analysis

PVplr is an R package designed with the intent to offer a variety of options and side-by-side comparisons when modeling outdoor PV time-series data for performance loss rate (PLR) analysis. Built as a part of the IEA-PVPS task 13 study on the determination and uncertainty of PLR calculation, the pac...

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Bibliographic Details
Published in:	2020 47th IEEE Photovoltaic Specialists Conference (PVSC) pp. 2086 - 2090
Main Authors:	Curran, Alan J., Burleyson, Tyler L., Rath, Kunal, Xin, Arthur S., Lindig, Sascha, Moser, David, Stein, Joshua, Bruckman, Laura S., French, Roger H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 14-06-2020
Subjects:	Analytical models Data models Degradation Meteorology performance loss rate Pipelines PLR reliability Uncertainty
Online Access:	Get full text
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Summary:	PVplr is an R package designed with the intent to offer a variety of options and side-by-side comparisons when modeling outdoor PV time-series data for performance loss rate (PLR) analysis. Built as a part of the IEA-PVPS task 13 study on the determination and uncertainty of PLR calculation, the package is designed to not to run a single method but to compare multiple commonly used methods on the same systems to determine system stability and identify biases in the analysis. The workflow is designed as a pipeline with steps of data cleaning, weather correction, time-series processing, and PLR determination, with multiple options at each step replicating commonly used methods or specific process created by other groups. Non-linear PLR evaluation capabilities have been added using piecewise linear modeling. Implementation of this pipeline in prior work has shown when 40 unique assumed linear PLR values are calculated for individual systems results can show significant variance depending on the steps chosen in data processing and modeling, highlighting the biases that can be induced by differences in analysis processes. Future updates to the package are planned that add more options to each step of the pipeline and allow python and R integration so python packages such as RdTools and PVlib-python can be used in conjunction with PVplr.
DOI:	10.1109/PVSC45281.2020.9300807